Bibliometric Review of Research in Financial Health
Bibliographic record
Abstract
Financial Health is a multi-dimensional concept that has taken shape over the past 20 years, and continues to develop. It arises from the incorporation of multidisciplinary components to the financial behaviour of individuals, allowing a good measurement of the quality of life of households and the economic stability of companies. In order to continue expanding the research associated with Financial Health, we have developed a bibliometric analysis that allows us to have a panoramic view of the most outstanding actors, institutions, authors, articles and countries that have the greatest importance worldwide. The research considers the use of VOSviewer software in order to model the bibliographic information associated with Financial Health and present it in an easy and simple manner to understand. The outcomes show a greater volume of publications from English speaking countries (United States, England, Australia and Canada) and the top 3 of the most cited sources is consistent with what we expected to get: 1 - Journal of Finance, 2 - Journal of Economics and Finance and 3 - American Economics Review. Finally, the institutions that most influence Financial Health research correspond to North American entities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.244 | 0.329 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".